Evaluating fatigue performance of Bailey asphalt mixtures containing natural river sand at varied strain and air void levels

Abstract

This study aims to investigate the efficiency of Bailey method of optimizing the fatigue life of asphalt mixtures when natural sand is included in the mix at two coarseness levels of aggregate gradations: Fine-graded (FG) and coarse-graded (CG). Three mixes which varied with the percentage of the natural river sand were prepared at each coarseness level, namely: Coarse-graded with quarry sand only (CG-QS), coarse-graded with natural sands only (CG-NS), coarse-graded with quarry and natural sands (CG-QNS), fine-graded with quarry sand only (FG-QS), fine-graded with natural sands only (FG-NS), and fine-graded with quarry and natural sands (FG-QNS). The portions of the natural sand either in CG-QNS and FG-QNS mixes were minimized as possible without violating the Bailey ratios. The beam fatigue (BF) test was used to evaluate the performance of each mixture at strain levels of 800, 1000, and 1200 microstrain. Also, to further investigate the air voids influence on the performance of the asphalt mixes, each mix was prepared at two air void levels: 7 and 4%. The sensitivities of the volumetric measures with the number of cycles to failure (N_f) were evaluated. The study’s findings indicate that N_f was generally decreasing with the increase of the natural sand in the mix at any strain level. Also, the 7% air voids mixes provided a lower N_f when compared to the 4% air voids mixes. The dust proportion (DP) was the most significant volumetric. The Bailey gradation method successfully provided a similar gradation coarseness for CG-QNS compared to CG-QS, which resulted in comparable N_f and indicates a similar aggregate interlock. The percentage decrease of CG-QNS containing 4% air voids was minimal, ranging from 1.92 to 5.46%, and a little higher at 7% air voids where N_f ranged from 11.48 to 13.23%.

Keywords:

• Bailey gradation
• asphalt mix
• natural sand
• performance test
• non-linear regression

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.